基于点袋理论的肝脏CT图像分析  

Hepatic CT Image Analysis Based on Bag of Keypoints

作　　者：张旭亚[1] 王俊[1] 

机构地区：[1]南京邮电大学图像处理与图像通信江苏省重点实验室,南京210003

出　　处：《北京生物医学工程》2011年第4期355-358,共4页Beijing Biomedical Engineering

基　　金：国家自然科学基金(60701002)资助

摘　　要：当前医学CT图像的病理识别过多地依靠医生经验,为了将机器辅助诊断引入病理识别中,本文采用点袋理论对肝癌和正常肝脏的CT图像进行分析研究。从100张CT图像中挑选出40张具有特征的CT图像,预处理后,先利用尺度不变特征变换(scale invariant feature,SIFT)描述CT图像特征点,再经过k均值(k-means)聚类,最后使用支持向量机(support vector machine,SVM)机器学习算法实现CT图像的识别和分类。实验结果表明,当采用Gaussian径向基(RBF)核运算时识别可取得85%的正确率。该研究说明点袋法有较好的分类效果和较好的发展空间。Current pathological recognition of medical CT images excessively relied on doctors＇ experience. This paper uses the theory of bag of keypoints to analyze the CT images of the carcinomatous and normal livers. Forty CT images which have disease features were picked from 100 CT images. CT images pretreated,the CT images＇ feature points were described out by scale invariant feature transformation（SIFT）. After that,it was processed with the k-means clustering. Finally, the support vector machine （SVM） machine learning algorithm was applied to the classification and recognition of CT images. Experimental results showed that the RBF kernel improve the correct rate of recognition to 85% . It is indicated that the bag of keypoints deserves good classification and larger practice value.

关 键 词：尺度不变特征变换 K均值 支持向量机 要点袋 医学图像分析 

分 类 号：R318.04[医药卫生—生物医学工程]